Document-Level Text Simplification Using Revision History Alignment
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Solution Overview
Problem
Existing text simplification models primarily focus on sentence-level editing and lack the ability to perform reliable edits based on larger than sentence-level contexts, such as document-level context, leading to inaccuracies and the introduction of factually incorrect content.
Innovation Solution
A text simplification framework that utilizes both sentence-level and document-level contexts, incorporating an alignment model to align revision histories and an edit identification model to categorize edits, enhancing the accuracy and reliability of text simplification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If text simplification models focus on sentence-level editing, then the model complexity remains manageable, but the accuracy and reliability of edits deteriorate due to lack of document-level context
Solution Approach 1:
The patent segments the text simplification task into multiple specialized modules: a document-level context module that processes entire documents to extract global context, and sentence-level editing modules that perform specific simplification operations. This segmentation allows each module to specialize in one aspect, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary document-level context processing layer that bridges the gap between raw input documents and sentence-level editing operations. This intermediary layer extracts and structures global context information, which is then fed to sentence-level editors to guide their operations, thereby improving reliability without requiring the sentence-level editors to directly handle entire documents.
2Reliability
If text simplification models use document-level context, then the accuracy of edits improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing of document-level context before sentence-level editing begins. The system pre-extracts global context, identifies document structure, and prepares guidance information in advance. This preliminary action ensures that when sentence-level editing occurs, the necessary contextual information is already available, reducing the need for repeated processing during actual simplification operations.
Solution Approach 2:
The patent divides the processing pipeline into distinct segments: a document-level context analysis stage that runs once per document, and sentence-level editing stages that process individual sentences. This segmentation allows computational resources to be efficiently allocated, with the expensive document-level processing performed once and its results reused across multiple sentence editing operations.
3Productivity
If text simplification models perform sentence-level editing only, then the processing speed remains high, but the ability to perform complex edits requiring broader context deteriorates
Solution Approach 1:
The patent segments editing capabilities into sentence-level operations for speed-critical tasks and document-level operations for complex, context-dependent tasks. The system automatically routes simple edits to fast sentence-level processors while directing complex edits requiring global context to the document-level module, optimizing both speed and versatility.
Solution Approach 2:
The patent introduces a context mediation layer that translates document-level semantic understanding into guidance for sentence-level editing operations. This intermediary enables sentence-level editors to perform more sophisticated edits by providing them with contextual constraints and preferences derived from document-level analysis, effectively extending their capability without sacrificing their processing speed advantage.
Data Source
AI summary
A method of training a text simplification model is provided. A training dataset including a first set of original textual samples and original revision histories and a second set of simplified textual samples and simplified revision histories is received via a data interface. A training pair including an original textual sample and corresponding original revision history from the first set and a counterpart simplified textual sample and corresponding simplified revision history from the second set are identified. An alignment label for a first revision in the corresponding original revision history and a second revision in the corresponding simplified revision history are generated using a neural network-based alignment model from a score. A revision category label for each of the first revision and second revision is generated using a neural network-based classification model. A neural network-based text simplification model is trained based on the updated training dataset.